This project aims to improve glaucoma management by applying novel pattern recognition techniques to improve the accurate prediction and detection of glaucomatous progression. The premise is that complex functional and structural tests in daily use by eye care providers contain hidden information that is not fully used in current analyses, and that advanced pattern recognition techniques can find and use that hidden information. The primary goals involve the use of mathematically rigorous techniques to discover patterns of defects and to track their changes in longitudinal series of perimetric and optical imaging data from up to 1800 glaucomatous and healthy eyes, available as the result of long-term NIH funding. With the interdisciplinary team of glaucoma and pattern recognition experts we have assembled, with our extensive NIH-supported database of eyes, and with the knowledge we have acquired in the optimal use of pattern recognition methods from previous NIH support, we believe the proposed work can enhance significantly the medical and surgical treatment of glaucoma and reduce the cost of glaucoma care. Moreover, improved techniques for predicting and detecting glaucomatous progression can be used for refined subject recruitment and to define endpoints for clinical trials of intraocular pressure-lowering and neuroprotective drugs.